Almiani, M., AbuGhazleh, A., Al-Rahayfeh, A., Atiewi, S. and Razaque, A. (2020) Deep Recurrent Neural Network for IoT Intrusion Detection System. Simulation Modelling Practice and Theory, 101, Article ID: 102031.
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has been cited by the following article:

• TITLE: Detection and Selection of Moving Objects in Video Images Based on Impulse and Recurrent Neural Networks

  AUTHORS: Ihar Yeuseyenka, Ihar Melnikau, Ihar Yemelyanov

  KEYWORDS: Automated System, Video Image, Pixel, Neuron, Neural Network

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.10 No.2, April 29, 2022

  ABSTRACT: The purpose of the article is to develop a methodology for automating the detection and selection of moving objects. The detection and separation of moving objects based on impulse and recurrence neural networks simulation. The result of the work is a developed motion detector based on impulse and recurrence neural networks and an automated system developed on the basis of this detector for detecting and separating moving objects and is ready for practical application. The feasibility of integrating the developed motion detector with Emgu CV (OpenCV) image processing package, multimedia framework functions, and DirectShow application programming interface were investigated. The proposed approach and software for the detection and separating of moving objects in video images using neural networks can be integrated into more sophisticated specialized computer-aided video surveillance systems, IoT (Internet of Things), IoV (Internet of Vehicles), etc.